Two-wheeled robot control method and apparatus, medium, program, and robot

Abstract

A robot control method includes calculating an optimal feedback gain, an optimal variable matrix, and uncertainty according to a first state variable and a first feedback gain of a robot. The optimal variable matrix represents a degree of a gain that a motion state of the robot has on a control mode of the robot. The method further includes calculating an angle deviation matrix and a noise deviation matrix according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and a second state variable of the robot, obtaining a control torque of the robot according to the second state variable, the optimal feedback gain, the angle deviation matrix, and the noise deviation matrix, and controlling the robot according to the control torque.

What is claimed is: 
     
         1 . A robot control method comprising:
 calculating an optimal feedback gain, an optimal variable matrix, and uncertainty according to a first state variable and a first feedback gain of a robot, the optimal variable matrix representing a degree of a gain that a motion state of the robot has on a control mode of the robot;   calculating an angle deviation matrix and a noise deviation matrix according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and a second state variable of the robot;   obtaining a control torque of the robot according to the second state variable, the optimal feedback gain, the angle deviation matrix, and the noise deviation matrix; and   controlling the robot according to the control torque.   
     
     
         2 . The method according to  claim 1 , wherein calculating the optimal feedback gain, the optimal variable matrix, and the uncertainty according to the first state variable and the first feedback gain of the robot includes:
 obtaining a j th  variable matrix according to the first state variable and a (j+1) th  feedback gain, j being an integer and having an initial value of 0;   determining a (j+2) th  feedback gain according to the (j+1) th  feedback gain;   updating j to j+1, and repeating operations of obtaining the j th  variable matrix and determining the (j+2) th  feedback gain until the j th  variable matrix reaches convergence;   in response to the j th  variable matrix reaching convergence, determining the j th  variable matrix as the optimal variable matrix, and determining the (j+1) th  feedback gain as the optimal feedback gain; and   determining the uncertainty according to the optimal variable matrix, the optimal feedback gain, and the first state variable.   
     
     
         3 . The method according to  claim 2 , wherein obtaining the j th  variable matrix according to the first state variable and the (j+1) th  feedback gain includes:
 determining a first intermediate matrix, a second intermediate matrix, and a third intermediate matrix according to the first state variable;   obtaining a first polynomial of an equation according to the (j+1) th  feedback gain, the first intermediate matrix, the second intermediate matrix, and the third intermediate matrix;   obtaining a second polynomial of the equation according to the (j+1) th  feedback gain and the first intermediate matrix; and   solving the equation with the first polynomial being equal to the second polynomial to obtain the j th  variable matrix.   
     
     
         4 . The method according to  claim 3 , further comprising:
 determining a fourth intermediate matrix according to the first state variable; and   in response to the second intermediate matrix, the third intermediate matrix, and the fourth intermediate matrix reaching full rank, performing operation of obtaining the first polynomial of the equation according to the (j+1) th  feedback gain, the first intermediate matrix, the second intermediate matrix, and the third intermediate matrix.   
     
     
         5 . The method according to  claim 4 , wherein:
 the first intermediate matrix includes a difference between a tensor product of the first state variable and the first state variable at a first moment and a tensor product of the first state variable and the first state variable at a second moment;   the second intermediate matrix includes an integral of a tensor product of the first state variable and the first state variable between the first moment and the second moment;   the third intermediate matrix includes an integral of a tensor product of the first state variable and a torque of the robot between the first moment and the second moment; and   the fourth intermediate matrix includes an integral of the first state variable between the first moment and the second moment.   
     
     
         6 . The method according to  claim 1 , wherein calculating the angle deviation matrix and the noise deviation matrix according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and the second state variable includes:
 determining an actual state variable according to the second state variable;   obtaining an error linear function according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and the actual state variable; and   obtaining the angle deviation matrix and the noise deviation matrix according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and the error linear function.   
     
     
         7 . The method according to  claim 6 , wherein obtaining the error linear function according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and the actual state variable includes:
 determining a first intermediate matrix, a second intermediate matrix, and a third intermediate matrix according to the actual state variable;   obtaining a first polynomial of an equation according to the optimal feedback gain, the optimal variable matrix, the uncertainty, the first intermediate matrix, the second intermediate matrix, and the third intermediate matrix;   obtaining a second polynomial of the equation according to the actual state variable and the first intermediate matrix; and   solving the equation with the first polynomial being equal to the second polynomial to obtain the error linear function.   
     
     
         8 . The method according to  claim 7 , further comprising:
 determining a fourth intermediate matrix according to the actual state variable; and   in response to the second intermediate matrix, the third intermediate matrix, and the fourth intermediate matrix reaching full rank, performing operation of obtaining the first polynomial of the equation according to the optimal feedback gain, the optimal variable matrix, the uncertainty, the first intermediate matrix, the second intermediate matrix, and the third intermediate matrix.   
     
     
         9 . The method according to  claim 8 , wherein:
 the first intermediate matrix includes a difference between a tensor product of the actual state variable and the actual state variable at a first moment and a tensor product of the actual state variable and the actual state variable at a second moment;   the second intermediate matrix includes an integral of a tensor product of the actual state variable and the actual state variable between the first moment and the second moment;   the third intermediate matrix includes an integral of a tensor product of the actual state variable and a torque of the robot between the first moment and the second moment; and   the fourth intermediate matrix includes an integral of the actual state variable between the first moment and the second moment.   
     
     
         10 . The method according to  claim 1 , wherein obtaining the control torque of the robot according to the second state variable, the optimal feedback gain, the angle deviation matrix, and the noise deviation matrix includes:
 obtaining a system compensation value according to the optimal feedback gain, the angle deviation matrix, and the noise deviation matrix; and   obtaining the control torque of the robot according to the optimal feedback gain, the second state variable, and the system compensation value.   
     
     
         11 . A robot comprising:
 one or more memories storing one or more computer programs; and   one or more processors configured to execute the one or more computer programs to:
 calculate an optimal feedback gain, an optimal variable matrix, and uncertainty according to a first state variable and a first feedback gain of the robot, the optimal variable matrix representing a degree of a gain that a motion state of the robot has on a control mode of the robot; 
 calculate an angle deviation matrix and a noise deviation matrix according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and a second state variable of the robot; 
 obtain control torque of the robot according to the second state variable, the optimal feedback gain, the angle deviation matrix, and the noise deviation matrix; and 
 control the robot according to the control torque. 
   
     
     
         12 . The robot according to  claim 11 , wherein the one or more processors are further configured to execute the one or more computer programs to:
 obtain a j th  variable matrix according to the first state variable and a (j+1) th  feedback gain, j being an integer and having an initial value of 0;   determine a (j+2) th  feedback gain according to the (j+1) th  feedback gain;   update j to j+1, and repeating operations of obtaining the j th  variable matrix and determining the (j+2) th  feedback gain until the j th  variable matrix reaches convergence;   in response to the j th  variable matrix reaching convergence, determine the j th  variable matrix as the optimal variable matrix, and determining the (j+1) th  feedback gain as the optimal feedback gain; and   determine the uncertainty according to the optimal variable matrix, the optimal feedback gain, and the first state variable.   
     
     
         13 . The robot according to  claim 12 , wherein the one or more processors are further configured to execute the one or more computer programs to:
 determine a first intermediate matrix, a second intermediate matrix, and a third intermediate matrix according to the first state variable;   obtain a first polynomial of an equation according to the (j+1) th  feedback gain, the first intermediate matrix, the second intermediate matrix, and the third intermediate matrix;   obtain a second polynomial of the equation according to the (j+1) th  feedback gain and the first intermediate matrix; and   solve the equation with the first polynomial being equal to the second polynomial to obtain the j th  variable matrix.   
     
     
         14 . The robot according to  claim 13 , wherein the one or more processors are further configured to execute the one or more computer programs to:
 determine a fourth intermediate matrix according to the first state variable; and   in response to the second intermediate matrix, the third intermediate matrix, and the fourth intermediate matrix reaching full rank, perform operation of obtaining the first polynomial of the equation according to the (j+1) th  feedback gain, the first intermediate matrix, the second intermediate matrix, and the third intermediate matrix.   
     
     
         15 . The robot according to  claim 14 , wherein:
 the first intermediate matrix includes a difference between a tensor product of the first state variable and the first state variable at a first moment and a tensor product of the first state variable and the first state variable at a second moment;   the second intermediate matrix includes an integral of a tensor product of the first state variable and the first state variable between the first moment and the second moment;   the third intermediate matrix includes an integral of a tensor product of the first state variable and a torque of the robot between the first moment and the second moment; and   the fourth intermediate matrix includes an integral of the first state variable between the first moment and the second moment.   
     
     
         16 . The robot according to  claim 11 , wherein the one or more processors are further configured to execute the one or more computer programs to:
 determine an actual state variable according to the second state variable;   obtain an error linear function according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and the actual state variable; and   obtain the angle deviation matrix and the noise deviation matrix according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and the error linear function.   
     
     
         17 . The robot according to  claim 16 , wherein the one or more processors are further configured to execute the one or more computer programs to:
 determine a first intermediate matrix, a second intermediate matrix, and a third intermediate matrix according to the actual state variable;   obtain a first polynomial of an equation according to the optimal feedback gain, the optimal variable matrix, the uncertainty, the first intermediate matrix, the second intermediate matrix, and the third intermediate matrix;   obtain a second polynomial of the equation according to the actual state variable and the first intermediate matrix; and   solve the equation with the first polynomial being equal to the second polynomial to obtain the error linear function.   
     
     
         18 . The robot according to  claim 17 , wherein the one or more processors are further configured to execute the one or more computer programs to:
 determine a fourth intermediate matrix according to the actual state variable; and   in response to the second intermediate matrix, the third intermediate matrix, and the fourth intermediate matrix reaching full rank, perform operation of obtaining the first polynomial of the equation according to the optimal feedback gain, the optimal variable matrix, the uncertainty, the first intermediate matrix, the second intermediate matrix, and the third intermediate matrix.   
     
     
         19 . The robot according to  claim 18 , wherein:
 the first intermediate matrix includes a difference between a tensor product of the actual state variable and the actual state variable at a first moment and a tensor product of the actual state variable and the actual state variable at a second moment;   the second intermediate matrix includes an integral of a tensor product of the actual state variable and the actual state variable between the first moment and the second moment;   the third intermediate matrix includes an integral of a tensor product of the actual state variable and a torque of the robot between the first moment and the second moment; and   the fourth intermediate matrix includes an integral of the actual state variable between the first moment and the second moment.   
     
     
         20 . A non-transitory computer-readable storage medium storing one or more computer programs that, when executed by one or more processors, cause the one or more processors to:
 calculate an optimal feedback gain, an optimal variable matrix, and uncertainty according to a first state variable and a first feedback gain of a robot, the optimal variable matrix representing a degree of a gain that a motion state of the robot has on a control mode of the robot;   calculate an angle deviation matrix and a noise deviation matrix according to the optimal feedback gain, the optimal variable matrix, the uncertainty, and a second state variable of the robot;   obtain control torque of the robot according to the second state variable, the optimal feedback gain, the angle deviation matrix, and the noise deviation matrix; and   control the robot according to the control torque.